Codebook

# Following codebook package's [vignette](https://cran.r-project.org/web/packages/codebook/vignettes/codebook_tutorial.html)

# Dependencies

library(tidyverse)
library(knitr)
library(kableExtra)
library(codebook)
library(labelled)
library(rio)

options(knitr.kable.NA = "/")


# Add labels ----

# read data
codebook_data <- read.csv("processed/4_data_participant_level_with_hand_scoring.csv")

# read data dictionary
dict <- read.csv("processed/5_data_dictionary.csv")

# add dictionary as labels
var_label(codebook_data) <- dict %>% 
  select(variable, label) %>% 
  dict_to_list()


# Add meta data ----

metadata(codebook_data)$name <- "Evaluative learning via deepfaked media"
metadata(codebook_data)$description <- "Across multiple experiments, we demonstrated that 'deepfakes' can establish automatic biases, self-reported evaluations, and behavioural intentions."
#metadata(codebook_data)$identifier <- "https://dx.doi.org/XXXXXXX"

metadata(codebook_data)$creator <- "Sean Hughes"
metadata(codebook_data)$citation <- "Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind."
metadata(codebook_data)$url <- "https://github.com/Sean-Hughes/DF-Impression-Formation--Video-and-Audio-"

# other meta data:  see https://schema.org/Dataset
metadata(codebook_data)$datePublished <- "2020"
metadata(codebook_data)$spatialCoverage <- "Online" 


# Create codebook ----

codebook(codebook_data)

Metadata

Description

Dataset name: Evaluative learning via deepfaked media

Across multiple experiments, we demonstrated that ‘deepfakes’ can establish automatic biases, self-reported evaluations, and behavioural intentions.

Metadata for search engines

x
subject
experiment
intervention_medium
source_valence
experiment_condition
exclude_subject
exclude_implausible_intervention_linger
intervention_linger_minutes
age
gender
IAT_D2
mean_self_reported_evaluation
mean_intentions
deepfake_detected
diagnosticity
demand
reactance
hypothesis_awareness
influence_awareness
aot_actively_openminded_thinking_sum
bcti_belief_in_conspiracy_sum
crt_analytic_thinking_sum
ocq_overclaiming_sum
ras_relgious_affliation_sum
rei_rational_sum
rei_experiential_sum
me_fake_news_awareness_sum
me_real_news_awareness_sum
me_fake_news_accuracy_sum
me_real_news_accuracy_sum
me_fake_news_sharing_sum
me_real_news_sharing_sum
aot_actively_openminded_thinking_pomp
bcti_belief_in_conspiracy_pomp
crt_analytic_thinking_pomp
ocq_overclaiming_pomp
ras_relgious_affliation_pomp
rei_rational_pomp
rei_experiential_pomp
me_fake_news_awareness_pomp
me_real_news_awareness_pomp
me_fake_news_accuracy_pomp
me_real_news_accuracy_pomp
me_fake_news_sharing_pomp
me_real_news_sharing_pomp
IAT_D2_recoded_for_source_valence
mean_self_reported_evaluation_recoded_for_source_valence
mean_intentions_recoded_for_source_valence
deepfake_concept_check
deepfake_detected_rater_1
deepfake_detected_rater_2
deepfake_concept_check_rater_1
deepfake_concept_check_rater_2
gender_self_describe
ethnicity
ethnicity_self_describe
location
education
employment
education_recoded
income
income_recoded
political_ideology_identity
political_ideology_social_issues
political_ideology_economic_issues
religious_affiliation_general
religious_affiliation_general_recoded
passed_iat_performance
complete_iat
complete_selfreport
complete_intentions
task_order
iat_block_order

#Variables

subject

Unique subject identifier

Distribution

Distribution of values for subject

Distribution of values for subject

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
subject Unique subject identifier character 0 1 1730 0 36 36 0

experiment

Experiment data was collected as part of

Distribution

Distribution of values for experiment

Distribution of values for experiment

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
experiment Experiment data was collected as part of numeric 0 1 1 4 6 3.739306 1.482387 ▆▇▇▅▅

intervention_medium

What medium did the intervention (whether deepfaked or genuine) take (e.g. video and audio vs just audio)

Distribution

Distribution of values for intervention_medium

Distribution of values for intervention_medium

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
intervention_medium What medium did the intervention (whether deepfaked or genuine) take (e.g. video and audio vs just audio) character 0 1 2 0 5 5 0

source_valence

What was the researcher-intended valence of the intervention? I.e. was the participant exposed to positive or negative messages?

Distribution

Distribution of values for source_valence

Distribution of values for source_valence

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
source_valence What was the researcher-intended valence of the intervention? I.e. was the participant exposed to positive or negative messages? character 0 1 2 0 8 8 0

experiment_condition

Was the intervention genuine or deepfaked content?

Distribution

Distribution of values for experiment_condition

Distribution of values for experiment_condition

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
experiment_condition Was the intervention genuine or deepfaked content? character 0 1 2 0 7 9 0

exclude_subject

Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance complete_iat complete_selfreport and complete_intentions

Distribution

Distribution of values for exclude_subject

Distribution of values for exclude_subject

0 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
exclude_subject Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance complete_iat complete_selfreport and complete_intentions logical 0 1 FAL: 1421, TRU: 309 0.1786127

exclude_implausible_intervention_linger

Should the participant be excluded (TRUE) or retained (FALSE) based on them spending not enough time (<1.5 minutes or too much time (>4.5 minutes) on the page that delivered the audio/video intervention.

Distribution

Distribution of values for exclude_implausible_intervention_linger

Distribution of values for exclude_implausible_intervention_linger

55 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
exclude_implausible_intervention_linger Should the participant be excluded (TRUE) or retained (FALSE) based on them spending not enough time (<1.5 minutes or too much time (>4.5 minutes) on the page that delivered the audio/video intervention. logical 55 0.9682081 TRU: 1555, FAL: 120 0.9283582

intervention_linger_minutes

Number of minutes spent viewing the page that delivered the intervention.

Distribution

Distribution of values for intervention_linger_minutes

Distribution of values for intervention_linger_minutes

55 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
intervention_linger_minutes Number of minutes spent viewing the page that delivered the intervention. numeric 55 0.9682081 0.028 2.7 2.7e+07 31805.05 920066.9 ▇▁▁▁▁

age

Participant age

Distribution

Distribution of values for age

Distribution of values for age

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
age Participant age numeric 0 1 18 29 70 31.24335 9.94172 ▇▆▃▁▁

gender

Participant gender

Distribution

Distribution of values for gender

Distribution of values for gender

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
gender Participant gender character 0 1 5 0 4 22 0

IAT_D2

The IAT captures automatic evaluations of Chris the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores following Greenwald et al 2003 and implemented using the IATScores R package. Briefly D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks

Distribution

Distribution of values for IAT_D2

Distribution of values for IAT_D2

141 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
IAT_D2 The IAT captures automatic evaluations of Chris the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores following Greenwald et al 2003 and implemented using the IATScores R package. Briefly D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks numeric 141 0.9184971 -1.2 0.23 1.6 0.2084644 0.3642037 ▁▃▇▃▁

mean_self_reported_evaluation

Mean self-reported evaluation (positive-negative good-bad pleasant-unpleasant) of Chris the character depicted in the intervention.

Distribution

Distribution of values for mean_self_reported_evaluation

Distribution of values for mean_self_reported_evaluation

98 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
mean_self_reported_evaluation Mean self-reported evaluation (positive-negative good-bad pleasant-unpleasant) of Chris the character depicted in the intervention. numeric 98 0.9433526 -3 0 3 -0.0859804 2.029025 ▇▃▃▅▇

mean_intentions

Mean behavioural intentions

Distribution

Distribution of values for mean_intentions

Distribution of values for mean_intentions

1485 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
mean_intentions Mean behavioural intentions numeric 1485 0.1416185 -2 -1 2 -0.9239184 1.052152 ▇▅▃▂▁

deepfake_detected

Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see “3 Instructions for raters.docx”). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.

Distribution

Distribution of values for deepfake_detected

Distribution of values for deepfake_detected

1120 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
deepfake_detected Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see “3 Instructions for raters.docx”). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE. logical 1120 0.3526012 FAL: 493, TRU: 117 0.1918033

diagnosticity

Question assessing whether people believe that the targets actions in the video/audio were representative of his ‘true’ character

Distribution

Distribution of values for diagnosticity

Distribution of values for diagnosticity

70 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
diagnosticity Question assessing whether people believe that the targets actions in the video/audio were representative of his ‘true’ character numeric 70 0.9595376 0 2 3 1.966867 0.7643258 ▁▃▁▇▃

demand

Assesssment of whether evaluations of the targer reflected how the person genuinely felt or if they simply responded in a way they thought the researchers wanted them to.

Distribution

Distribution of values for demand

Distribution of values for demand

131 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
demand Assesssment of whether evaluations of the targer reflected how the person genuinely felt or if they simply responded in a way they thought the researchers wanted them to. logical 131 0.9242775 FAL: 1585, TRU: 14 0.0087555

reactance

Assesssment of whether evaluations of the targer reflected how the person genuinely felt or if they responded in the opposite way than they thought the researchers wanted them to.

Distribution

Distribution of values for reactance

Distribution of values for reactance

131 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
reactance Assesssment of whether evaluations of the targer reflected how the person genuinely felt or if they responded in the opposite way than they thought the researchers wanted them to. logical 131 0.9242775 FAL: 1537, TRU: 62 0.0387742

hypothesis_awareness

Question asking what participants thought the experiment was about (i.e. what the experimenter’s agenda was in the study)

Distribution

Distribution of values for hypothesis_awareness

Distribution of values for hypothesis_awareness

1672 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
hypothesis_awareness Question asking what participants thought the experiment was about (i.e. what the experimenter’s agenda was in the study) logical 1672 0.033526 FAL: 58 0

influence_awareness

Question asking if participants thought the video/audio influenced how much they liked or disliked the target

Distribution

Distribution of values for influence_awareness

Distribution of values for influence_awareness

946 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
influence_awareness Question asking if participants thought the video/audio influenced how much they liked or disliked the target logical 946 0.4531792 TRU: 697, FAL: 87 0.8890306

aot_actively_openminded_thinking_sum

Scale assessing actively open minded thinking about evidence (Pennycook G. Cheyne J. A. Koehler D. & Fugelsang J. A. (2019). On the belief that beliefs should change according to evidence: Implications for conspiratorial moral paranormal political religious and science beliefs.)

Distribution

Distribution of values for aot_actively_openminded_thinking_sum

Distribution of values for aot_actively_openminded_thinking_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
aot_actively_openminded_thinking_sum Scale assessing actively open minded thinking about evidence (Pennycook G. Cheyne J. A. Koehler D. & Fugelsang J. A. (2019). On the belief that beliefs should change according to evidence: Implications for conspiratorial moral paranormal political religious and science beliefs.) numeric 1486 0.1410405 22 38 48 37.40164 5.934706 ▂▅▇▇▆

bcti_belief_in_conspiracy_sum

The Belief in Conspiracy Theories Inventory (BCTI, Swami et al., 2010) which assessed general conspiracist ideation or belief in conspiracy theories

Distribution

Distribution of values for bcti_belief_in_conspiracy_sum

Distribution of values for bcti_belief_in_conspiracy_sum

1481 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
bcti_belief_in_conspiracy_sum The Belief in Conspiracy Theories Inventory (BCTI, Swami et al., 2010) which assessed general conspiracist ideation or belief in conspiracy theories numeric 1481 0.1439306 15 51 129 54.04819 23.60011 ▇▇▆▂▁

crt_analytic_thinking_sum

The revised Cognitive Reflection Test (RCRT). The Revised Cognitive Reflection Test originally developed by Toplak West and Stanovich (2014) and subsequently revised by Bronstein Pennycook Bear Rand and Cannon (2019) was used to measure analytic thinking ability.

Distribution

Distribution of values for crt_analytic_thinking_sum

Distribution of values for crt_analytic_thinking_sum

1236 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
crt_analytic_thinking_sum The revised Cognitive Reflection Test (RCRT). The Revised Cognitive Reflection Test originally developed by Toplak West and Stanovich (2014) and subsequently revised by Bronstein Pennycook Bear Rand and Cannon (2019) was used to measure analytic thinking ability. numeric 1236 0.2855491 0 4 7 3.953441 1.848155 ▃▃▇▅▆

ocq_overclaiming_sum

The overclaiming questionnaire adapted from Paulhus et al. (2003).

Distribution

Distribution of values for ocq_overclaiming_sum

Distribution of values for ocq_overclaiming_sum

1480 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
ocq_overclaiming_sum The overclaiming questionnaire adapted from Paulhus et al. (2003). numeric 1480 0.1445087 10 72 143 71.516 29.66252 ▃▇▇▆▂

ras_relgious_affliation_sum

The Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014).

Distribution

Distribution of values for ras_relgious_affliation_sum

Distribution of values for ras_relgious_affliation_sum

1194 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
ras_relgious_affliation_sum The Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). numeric 1194 0.3098266 8 21 40 20.77799 8.32119 ▇▆▇▅▂

rei_rational_sum

The rational scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles.

Distribution

Distribution of values for rei_rational_sum

Distribution of values for rei_rational_sum

1235 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
rei_rational_sum The rational scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. numeric 1235 0.2861272 15 52 70 50.79394 10.68324 ▁▂▅▇▅

rei_experiential_sum

The experiential scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles.

Distribution

Distribution of values for rei_experiential_sum

Distribution of values for rei_experiential_sum

1235 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
rei_experiential_sum The experiential scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. numeric 1235 0.2861272 16 49 70 48.4101 8.768012 ▁▂▇▇▂

me_fake_news_awareness_sum

Media evaluation task. Awareness sum score for the fake news items

Distribution

Distribution of values for me_fake_news_awareness_sum

Distribution of values for me_fake_news_awareness_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_fake_news_awareness_sum Media evaluation task. Awareness sum score for the fake news items numeric 1486 0.1410405 0 1 6 1.413934 1.191866 ▇▃▂▁▁

me_real_news_awareness_sum

Media evaluation task. Awareness sum score for the real news items

Distribution

Distribution of values for me_real_news_awareness_sum

Distribution of values for me_real_news_awareness_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_real_news_awareness_sum Media evaluation task. Awareness sum score for the real news items numeric 1486 0.1410405 0 0 4 0.2008197 0.5178296 ▇▁▁▁▁

me_fake_news_accuracy_sum

Media evaluation task. Accuracy sum score for the fake news items

Distribution

Distribution of values for me_fake_news_accuracy_sum

Distribution of values for me_fake_news_accuracy_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_fake_news_accuracy_sum Media evaluation task. Accuracy sum score for the fake news items numeric 1486 0.1410405 7 17 23 16.72131 2.614497 ▁▂▇▇▂

me_real_news_accuracy_sum

Media evaluation task. Accuracy sum score for the real news items

Distribution

Distribution of values for me_real_news_accuracy_sum

Distribution of values for me_real_news_accuracy_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_real_news_accuracy_sum Media evaluation task. Accuracy sum score for the real news items numeric 1486 0.1410405 6 9 20 9.127049 2.460437 ▇▆▂▁▁

me_fake_news_sharing_sum

Media evaluation task. Sharing sum score for the fake news items

Distribution

Distribution of values for me_fake_news_sharing_sum

Distribution of values for me_fake_news_sharing_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_fake_news_sharing_sum Media evaluation task. Sharing sum score for the fake news items numeric 1486 0.1410405 0 1 6 1.561475 1.768585 ▇▂▁▂▁

me_real_news_sharing_sum

Media evaluation task. Accuracy sum score for the real news items

Distribution

Distribution of values for me_real_news_sharing_sum

Distribution of values for me_real_news_sharing_sum

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_real_news_sharing_sum Media evaluation task. Accuracy sum score for the real news items numeric 1486 0.1410405 0 0 6 0.454918 0.9739489 ▇▁▁▁▁

aot_actively_openminded_thinking_pomp

POMP scores derived from scale assessing actively open minded thinking about evidence (Pennycook G. Cheyne J. A. Koehler D. & Fugelsang J. A. (2019). On the belief that beliefs should change according to evidence: Implications for conspiratorial moral paranormal political religious and science beliefs.)

Distribution

Distribution of values for aot_actively_openminded_thinking_pomp

Distribution of values for aot_actively_openminded_thinking_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
aot_actively_openminded_thinking_pomp POMP scores derived from scale assessing actively open minded thinking about evidence (Pennycook G. Cheyne J. A. Koehler D. & Fugelsang J. A. (2019). On the belief that beliefs should change according to evidence: Implications for conspiratorial moral paranormal political religious and science beliefs.) numeric 1486 0.1410405 35 75 100 73.48361 14.87464 ▂▅▇▇▆

bcti_belief_in_conspiracy_pomp

POMP scores derived from the Belief in Conspiracy Theories Inventory (BCTI, Swami et al., 2010) which assessed general conspiracist ideation or belief in conspiracy theories

Distribution

Distribution of values for bcti_belief_in_conspiracy_pomp

Distribution of values for bcti_belief_in_conspiracy_pomp

1481 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
bcti_belief_in_conspiracy_pomp POMP scores derived from the Belief in Conspiracy Theories Inventory (BCTI, Swami et al., 2010) which assessed general conspiracist ideation or belief in conspiracy theories numeric 1481 0.1439306 0 30 95 32.56627 19.67961 ▇▇▆▂▁

crt_analytic_thinking_pomp

POMP scores derived from revised Cognitive Reflection Test (RCRT). The Revised Cognitive Reflection Test originally developed by Toplak West and Stanovich (2014) and subsequently revised by Bronstein Pennycook Bear Rand and Cannon (2019) was used to measure analytic thinking ability.

Distribution

Distribution of values for crt_analytic_thinking_pomp

Distribution of values for crt_analytic_thinking_pomp

1236 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
crt_analytic_thinking_pomp POMP scores derived from revised Cognitive Reflection Test (RCRT). The Revised Cognitive Reflection Test originally developed by Toplak West and Stanovich (2014) and subsequently revised by Bronstein Pennycook Bear Rand and Cannon (2019) was used to measure analytic thinking ability. numeric 1236 0.2855491 0 57 100 56.47368 26.39254 ▃▃▇▅▆

ocq_overclaiming_pomp

POMP scores derived from the overclaiming questionnaire adapted from Paulhus et al. (2003).

Distribution

Distribution of values for ocq_overclaiming_pomp

Distribution of values for ocq_overclaiming_pomp

1480 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
ocq_overclaiming_pomp POMP scores derived from the overclaiming questionnaire adapted from Paulhus et al. (2003). numeric 1480 0.1445087 6 40 79 39.728 16.45628 ▃▇▇▆▂

ras_relgious_affliation_pomp

POMP scores derived from the Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014).

Distribution

Distribution of values for ras_relgious_affliation_pomp

Distribution of values for ras_relgious_affliation_pomp

1194 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
ras_relgious_affliation_pomp POMP scores derived from the Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). numeric 1194 0.3098266 0 41 100 39.91978 26.0168 ▇▆▇▅▂

rei_rational_pomp

POMP scores for the rational scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles.

Distribution

Distribution of values for rei_rational_pomp

Distribution of values for rei_rational_pomp

1235 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
rei_rational_pomp POMP scores for the rational scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. numeric 1235 0.2861272 -4 27 42 25.67879 8.894561 ▁▂▅▇▅

rei_experiential_pomp

POMP scores for the experiential scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles.

Distribution

Distribution of values for rei_experiential_pomp

Distribution of values for rei_experiential_pomp

1235 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
rei_experiential_pomp POMP scores for the experiential scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. numeric 1235 0.2861272 -3 24 42 23.68687 7.323976 ▁▂▇▇▂

me_fake_news_awareness_pomp

Media evaluation task. POMP score for the fake news items (awareness)

Distribution

Distribution of values for me_fake_news_awareness_pomp

Distribution of values for me_fake_news_awareness_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_fake_news_awareness_pomp Media evaluation task. POMP score for the fake news items (awareness) numeric 1486 0.1410405 -50 -46 -25 -44.29508 4.888364 ▇▃▂▁▁

me_real_news_awareness_pomp

Media evaluation task. POMP score for the real news items (awareness)

Distribution

Distribution of values for me_real_news_awareness_pomp

Distribution of values for me_real_news_awareness_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_real_news_awareness_pomp Media evaluation task. POMP score for the real news items (awareness) numeric 1486 0.1410405 -50 -50 -33 -49.19262 2.102269 ▇▁▁▁▁

me_fake_news_accuracy_pomp

Media evaluation task. POMP score for the fake news items (accuracy)

Distribution

Distribution of values for me_fake_news_accuracy_pomp

Distribution of values for me_fake_news_accuracy_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_fake_news_accuracy_pomp Media evaluation task. POMP score for the fake news items (accuracy) numeric 1486 0.1410405 -14 14 31 13.11475 7.217602 ▁▂▆▇▁

me_real_news_accuracy_pomp

Media evaluation task. POMP score for the real news items (accuracy)

Distribution

Distribution of values for me_real_news_accuracy_pomp

Distribution of values for me_real_news_accuracy_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_real_news_accuracy_pomp Media evaluation task. POMP score for the real news items (accuracy) numeric 1486 0.1410405 -17 -8 22 -8.032787 6.895379 ▇▆▂▁▁

me_fake_news_sharing_pomp

Media evaluation task. POMP score for the fake news items (sharing)

Distribution

Distribution of values for me_fake_news_sharing_pomp

Distribution of values for me_fake_news_sharing_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_fake_news_sharing_pomp Media evaluation task. POMP score for the fake news items (sharing) numeric 1486 0.1410405 -50 -46 -25 -43.57787 7.389103 ▇▂▁▂▁

me_real_news_sharing_pomp

Media evaluation task. POMP score for the real news items (sharing)

Distribution

Distribution of values for me_real_news_sharing_pomp

Distribution of values for me_real_news_sharing_pomp

1486 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
me_real_news_sharing_pomp Media evaluation task. POMP score for the real news items (sharing) numeric 1486 0.1410405 -50 -50 -25 -48.15984 3.989062 ▇▁▁▁▁

IAT_D2_recoded_for_source_valence

IAT_D2 recoded for source_valence. If source_valence == “negative”, IAT_D2*-1, otherwise IAT_D2.

Distribution

Distribution of values for IAT_D2_recoded_for_source_valence

Distribution of values for IAT_D2_recoded_for_source_valence

141 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
IAT_D2_recoded_for_source_valence IAT_D2 recoded for source_valence. If source_valence == “negative”, IAT_D2*-1, otherwise IAT_D2. numeric 141 0.9184971 -0.94 0.2 1.6 0.1854097 0.3764714 ▁▅▇▃▁

mean_self_reported_evaluation_recoded_for_source_valence

mean_self_reported_evaluation recoded for source_valence. If source_valence == “negative”, mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.

Distribution

Distribution of values for mean_self_reported_evaluation_recoded_for_source_valence

Distribution of values for mean_self_reported_evaluation_recoded_for_source_valence

98 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
mean_self_reported_evaluation_recoded_for_source_valence mean_self_reported_evaluation recoded for source_valence. If source_valence == “negative”, mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation. numeric 98 0.9433526 -3 2 3 1.568836 1.289025 ▁▁▂▃▇

mean_intentions_recoded_for_source_valence

mean_intentions recoded for source_valence. If source_valence == “negative”, mean_intentions*-1, otherwise mean_intentions.

Distribution

Distribution of values for mean_intentions_recoded_for_source_valence

Distribution of values for mean_intentions_recoded_for_source_valence

1485 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
mean_intentions_recoded_for_source_valence mean_intentions recoded for source_valence. If source_valence == “negative”, mean_intentions*-1, otherwise mean_intentions. numeric 1485 0.1416185 -2 0.67 2 0.5263673 1.298441 ▂▃▅▂▇

deepfake_concept_check

Question assessin if participants were aware of the concept of Deepfaking before taking part in the study

Distribution

Distribution of values for deepfake_concept_check

Distribution of values for deepfake_concept_check

1294 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
deepfake_concept_check Question assessin if participants were aware of the concept of Deepfaking before taking part in the study logical 1294 0.2520231 TRU: 234, FAL: 202 0.5366972

deepfake_detected_rater_1

Ratings indicating whether a participant detected that they were exposed to Deepfaked content during the study. Ratings obtained from Rater 1

Distribution

Distribution of values for deepfake_detected_rater_1

Distribution of values for deepfake_detected_rater_1

1120 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
deepfake_detected_rater_1 Ratings indicating whether a participant detected that they were exposed to Deepfaked content during the study. Ratings obtained from Rater 1 logical 1120 0.3526012 FAL: 462, TRU: 148 0.242623

deepfake_detected_rater_2

Ratings indicating whether a participant detected that they were exposed to Deepfaked content during the study. Ratings obtained from Rater 2

Distribution

Distribution of values for deepfake_detected_rater_2

Distribution of values for deepfake_detected_rater_2

1120 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
deepfake_detected_rater_2 Ratings indicating whether a participant detected that they were exposed to Deepfaked content during the study. Ratings obtained from Rater 2 logical 1120 0.3526012 FAL: 476, TRU: 134 0.2196721

deepfake_concept_check_rater_1

Ratings indicating if participants were aware of the concept of a Deepfake before taking part in the study. Ratings obtained from Rater 1

Distribution

Distribution of values for deepfake_concept_check_rater_1

Distribution of values for deepfake_concept_check_rater_1

1294 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
deepfake_concept_check_rater_1 Ratings indicating if participants were aware of the concept of a Deepfake before taking part in the study. Ratings obtained from Rater 1 logical 1294 0.2520231 TRU: 247, FAL: 189 0.5665138

deepfake_concept_check_rater_2

Ratings indicating if participants were aware of the concept of a Deepfake before taking part in the study. Ratings obtained from Rater 2

Distribution

Distribution of values for deepfake_concept_check_rater_2

Distribution of values for deepfake_concept_check_rater_2

1294 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
deepfake_concept_check_rater_2 Ratings indicating if participants were aware of the concept of a Deepfake before taking part in the study. Ratings obtained from Rater 2 logical 1294 0.2520231 TRU: 247, FAL: 189 0.5665138

gender_self_describe

Open ended gender

Distribution

Distribution of values for gender_self_describe

Distribution of values for gender_self_describe

1725 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
gender_self_describe Open ended gender character 1725 0.0028902 1 0 4 4 0

ethnicity

Ethnicity

Distribution

Distribution of values for ethnicity

Distribution of values for ethnicity

1189 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
ethnicity Ethnicity character 1189 0.3127168 7 0 5 25 0

ethnicity_self_describe

Open ended ethnicity

Distribution

Distribution of values for ethnicity_self_describe

Distribution of values for ethnicity_self_describe

1701 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
ethnicity_self_describe Open ended ethnicity character 1701 0.016763 19 0 4 40 0

location

Location

Distribution

Distribution of values for location

Distribution of values for location

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
location Location character 1190 0.3121387 18 0 5 24 0

education

Education

Distribution

Distribution of values for education

Distribution of values for education

1189 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
education Education character 1189 0.3127168 8 0 7 21 0

employment

Employment status

Distribution

Distribution of values for employment

Distribution of values for employment

1189 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
employment Employment status character 1189 0.3127168 10 0 7 24 0

education_recoded

Education recoded into seven groups

Distribution

Distribution of values for education_recoded

Distribution of values for education_recoded

1189 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
education_recoded Education recoded into seven groups numeric 1189 0.3127168 1 5 7 4.072089 1.526427 ▅▅▁▇▃

income

Income level

Distribution

Distribution of values for income

Distribution of values for income

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
income Income level character 1190 0.3121387 10 0 12 20 0

income_recoded

Income level recoded into 7 groups

Distribution

Distribution of values for income_recoded

Distribution of values for income_recoded

1238 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
income_recoded Income level recoded into 7 groups numeric 1238 0.2843931 1 3 8 2.943089 1.503835 ▇▆▇▁▁

political_ideology_identity

4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about the importance of political ideology to one’s self-identity

Distribution

Distribution of values for political_ideology_identity

Distribution of values for political_ideology_identity

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
political_ideology_identity 4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about the importance of political ideology to one’s self-identity numeric 1190 0.3121387 -3 1 3 0.8574074 1.544648 ▂▂▃▇▇

political_ideology_social_issues

4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about how liberal vs. conservative they are when it comes to social issues.

Distribution

Distribution of values for political_ideology_social_issues

Distribution of values for political_ideology_social_issues

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
political_ideology_social_issues 4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about how liberal vs. conservative they are when it comes to social issues. numeric 1190 0.3121387 -2 -1 2 -0.637037 1.023707 ▅▇▆▂▁

political_ideology_economic_issues

4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about how liberal vs. conservative they are when it comes to economic issues.

Distribution

Distribution of values for political_ideology_economic_issues

Distribution of values for political_ideology_economic_issues

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
political_ideology_economic_issues 4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about how liberal vs. conservative they are when it comes to economic issues. numeric 1190 0.3121387 -2 0 2 -0.4148148 1.032929 ▃▇▇▃▁

religious_affiliation_general

Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). Raw responses

Distribution

Distribution of values for religious_affiliation_general

Distribution of values for religious_affiliation_general

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
religious_affiliation_general Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). Raw responses character 1190 0.3121387 10 0 4 16 0

religious_affiliation_general_recoded

Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). Participants categorised into one of three groups (Agnostic Atheist Religious)

Distribution

Distribution of values for religious_affiliation_general_recoded

Distribution of values for religious_affiliation_general_recoded

1190 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
religious_affiliation_general_recoded Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). Participants categorised into one of three groups (Agnostic Atheist Religious) character 1190 0.3121387 3 0 7 9 0

passed_iat_performance

Mark participants for exclusion if their total error rate is >30% their error rate in any one block is >40% or if >10% RTs are <300ms.

Distribution

Distribution of values for passed_iat_performance

Distribution of values for passed_iat_performance

122 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
passed_iat_performance Mark participants for exclusion if their total error rate is >30% their error rate in any one block is >40% or if >10% RTs are <300ms. logical 122 0.9294798 TRU: 1440, FAL: 168 0.8955224

complete_iat

Complete IAT data (used for exclusions)

Distribution

Distribution of values for complete_iat

Distribution of values for complete_iat

122 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
complete_iat Complete IAT data (used for exclusions) character 122 0.9294798 3 0 6 8 0

complete_selfreport

Complete self reported evaluations data (used for exclusions)

Distribution

Distribution of values for complete_selfreport

Distribution of values for complete_selfreport

98 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
complete_selfreport Complete self reported evaluations data (used for exclusions) character 98 0.9433526 2 0 6 8 0

complete_intentions

Complete behavioural intentions data (used for exclusions)

Distribution

Distribution of values for complete_intentions

Distribution of values for complete_intentions

1485 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
complete_intentions Complete behavioural intentions data (used for exclusions) character 1485 0.1416185 2 0 6 8 0

task_order

Did the particiapant complete the self-reported evaluations or the IAT first?

Distribution

Distribution of values for task_order

Distribution of values for task_order

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
task_order Did the particiapant complete the self-reported evaluations or the IAT first? character 0 1 2 0 9 25 0

iat_block_order

Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g. if source_valence was negative did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?

Distribution

Distribution of values for iat_block_order

Distribution of values for iat_block_order

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
iat_block_order Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g. if source_valence was negative did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)? character 0 1 2 0 31 33 0

Missingness report

Codebook table

JSON-LD metadata The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Evaluative learning via deepfaked media",
  "description": "Across multiple experiments, we demonstrated that 'deepfakes' can establish automatic biases, self-reported evaluations, and behavioural intentions.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "creator": "Sean Hughes",
  "citation": "Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind.",
  "url": "https://github.com/Sean-Hughes/DF-Impression-Formation--Video-and-Audio-",
  "datePublished": "2020",
  "spatialCoverage": "Online",
  "keywords": ["subject", "experiment", "intervention_medium", "source_valence", "experiment_condition", "exclude_subject", "exclude_implausible_intervention_linger", "intervention_linger_minutes", "age", "gender", "IAT_D2", "mean_self_reported_evaluation", "mean_intentions", "deepfake_detected", "diagnosticity", "demand", "reactance", "hypothesis_awareness", "influence_awareness", "aot_actively_openminded_thinking_sum", "bcti_belief_in_conspiracy_sum", "crt_analytic_thinking_sum", "ocq_overclaiming_sum", "ras_relgious_affliation_sum", "rei_rational_sum", "rei_experiential_sum", "me_fake_news_awareness_sum", "me_real_news_awareness_sum", "me_fake_news_accuracy_sum", "me_real_news_accuracy_sum", "me_fake_news_sharing_sum", "me_real_news_sharing_sum", "aot_actively_openminded_thinking_pomp", "bcti_belief_in_conspiracy_pomp", "crt_analytic_thinking_pomp", "ocq_overclaiming_pomp", "ras_relgious_affliation_pomp", "rei_rational_pomp", "rei_experiential_pomp", "me_fake_news_awareness_pomp", "me_real_news_awareness_pomp", "me_fake_news_accuracy_pomp", "me_real_news_accuracy_pomp", "me_fake_news_sharing_pomp", "me_real_news_sharing_pomp", "IAT_D2_recoded_for_source_valence", "mean_self_reported_evaluation_recoded_for_source_valence", "mean_intentions_recoded_for_source_valence", "deepfake_concept_check", "deepfake_detected_rater_1", "deepfake_detected_rater_2", "deepfake_concept_check_rater_1", "deepfake_concept_check_rater_2", "gender_self_describe", "ethnicity", "ethnicity_self_describe", "location", "education", "employment", "education_recoded", "income", "income_recoded", "political_ideology_identity", "political_ideology_social_issues", "political_ideology_economic_issues", "religious_affiliation_general", "religious_affiliation_general_recoded", "passed_iat_performance", "complete_iat", "complete_selfreport", "complete_intentions", "task_order", "iat_block_order"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "subject",
      "description": "Unique subject identifier",
      "@type": "propertyValue"
    },
    {
      "name": "experiment",
      "description": "Experiment data was collected as part of",
      "@type": "propertyValue"
    },
    {
      "name": "intervention_medium",
      "description": "What medium did the intervention (whether deepfaked or genuine) take (e.g. video and audio vs just audio)",
      "@type": "propertyValue"
    },
    {
      "name": "source_valence",
      "description": "What was the researcher-intended valence of the intervention? I.e. was the participant exposed to positive or negative messages?",
      "@type": "propertyValue"
    },
    {
      "name": "experiment_condition",
      "description": "Was the intervention genuine or deepfaked content?",
      "@type": "propertyValue"
    },
    {
      "name": "exclude_subject",
      "description": "Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance complete_iat complete_selfreport and complete_intentions",
      "@type": "propertyValue"
    },
    {
      "name": "exclude_implausible_intervention_linger",
      "description": "Should the participant be excluded (TRUE) or retained (FALSE) based on them spending not enough time (<1.5 minutes or too much time (>4.5 minutes) on the page that delivered the audio/video intervention.",
      "@type": "propertyValue"
    },
    {
      "name": "intervention_linger_minutes",
      "description": "Number of minutes spent viewing the page that delivered the intervention.",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Participant age",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Participant gender",
      "@type": "propertyValue"
    },
    {
      "name": "IAT_D2",
      "description": "The IAT captures automatic evaluations of Chris the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores following Greenwald et al 2003 and implemented using the IATScores R package. Briefly D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks",
      "@type": "propertyValue"
    },
    {
      "name": "mean_self_reported_evaluation",
      "description": "Mean self-reported evaluation (positive-negative good-bad pleasant-unpleasant) of Chris the character depicted in the intervention.",
      "@type": "propertyValue"
    },
    {
      "name": "mean_intentions",
      "description": "Mean behavioural intentions",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_detected",
      "description": "Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see \"3 Instructions for raters.docx\"). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.",
      "@type": "propertyValue"
    },
    {
      "name": "diagnosticity",
      "description": "Question assessing whether people believe that the targets actions in the video/audio were representative of his 'true' character",
      "@type": "propertyValue"
    },
    {
      "name": "demand",
      "description": "Assesssment of whether evaluations of the targer reflected how the person genuinely felt or if they simply responded in a way they thought the researchers wanted them to.",
      "@type": "propertyValue"
    },
    {
      "name": "reactance",
      "description": "Assesssment of whether evaluations of the targer reflected how the person genuinely felt or if they responded in the opposite way than they thought the researchers wanted them to.",
      "@type": "propertyValue"
    },
    {
      "name": "hypothesis_awareness",
      "description": "Question asking what participants thought the experiment was about (i.e. what the experimenter's agenda was in the study)",
      "@type": "propertyValue"
    },
    {
      "name": "influence_awareness",
      "description": "Question asking if participants thought the video/audio influenced how much they liked or disliked the target",
      "@type": "propertyValue"
    },
    {
      "name": "aot_actively_openminded_thinking_sum",
      "description": "Scale assessing actively open minded thinking about evidence (Pennycook G. Cheyne J. A. Koehler D. & Fugelsang J. A. (2019). On the belief that beliefs should change according to evidence: Implications for conspiratorial moral paranormal political religious and science beliefs.)",
      "@type": "propertyValue"
    },
    {
      "name": "bcti_belief_in_conspiracy_sum",
      "description": "The Belief in Conspiracy Theories Inventory (BCTI, Swami et al., 2010) which assessed general conspiracist ideation or belief in conspiracy theories ",
      "@type": "propertyValue"
    },
    {
      "name": "crt_analytic_thinking_sum",
      "description": "The revised Cognitive Reflection Test (RCRT). The Revised Cognitive Reflection Test originally developed by Toplak West and Stanovich (2014) and subsequently revised by Bronstein Pennycook Bear Rand and Cannon (2019) was used to measure analytic thinking ability. ",
      "@type": "propertyValue"
    },
    {
      "name": "ocq_overclaiming_sum",
      "description": "The overclaiming questionnaire adapted from Paulhus et al. (2003). ",
      "@type": "propertyValue"
    },
    {
      "name": "ras_relgious_affliation_sum",
      "description": "The Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). ",
      "@type": "propertyValue"
    },
    {
      "name": "rei_rational_sum",
      "description": "The rational scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. ",
      "@type": "propertyValue"
    },
    {
      "name": "rei_experiential_sum",
      "description": "The experiential scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. ",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_awareness_sum",
      "description": "Media evaluation task. Awareness sum score for the fake news items",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_awareness_sum",
      "description": "Media evaluation task. Awareness sum score for the real news items",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_accuracy_sum",
      "description": "Media evaluation task. Accuracy sum score for the fake news items",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_accuracy_sum",
      "description": "Media evaluation task. Accuracy sum score for the real news items",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_sharing_sum",
      "description": "Media evaluation task. Sharing sum score for the fake news items",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_sharing_sum",
      "description": "Media evaluation task. Accuracy sum score for the real news items",
      "@type": "propertyValue"
    },
    {
      "name": "aot_actively_openminded_thinking_pomp",
      "description": "POMP scores derived from scale assessing actively open minded thinking about evidence (Pennycook G. Cheyne J. A. Koehler D. & Fugelsang J. A. (2019). On the belief that beliefs should change according to evidence: Implications for conspiratorial moral paranormal political religious and science beliefs.)",
      "@type": "propertyValue"
    },
    {
      "name": "bcti_belief_in_conspiracy_pomp",
      "description": "POMP scores derived from the Belief in Conspiracy Theories Inventory (BCTI, Swami et al., 2010) which assessed general conspiracist ideation or belief in conspiracy theories ",
      "@type": "propertyValue"
    },
    {
      "name": "crt_analytic_thinking_pomp",
      "description": "POMP scores derived from revised Cognitive Reflection Test (RCRT). The Revised Cognitive Reflection Test originally developed by Toplak West and Stanovich (2014) and subsequently revised by Bronstein Pennycook Bear Rand and Cannon (2019) was used to measure analytic thinking ability. ",
      "@type": "propertyValue"
    },
    {
      "name": "ocq_overclaiming_pomp",
      "description": "POMP scores derived from the overclaiming questionnaire adapted from Paulhus et al. (2003). ",
      "@type": "propertyValue"
    },
    {
      "name": "ras_relgious_affliation_pomp",
      "description": "POMP scores derived from the Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). ",
      "@type": "propertyValue"
    },
    {
      "name": "rei_rational_pomp",
      "description": "POMP scores for the rational scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. ",
      "@type": "propertyValue"
    },
    {
      "name": "rei_experiential_pomp",
      "description": "POMP scores for the experiential scale. Derived from the Rational-Experiential Inventory (REI) developed by Pacini and Epstein (1999) designed to measure individual differences in processing styles. ",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_awareness_pomp",
      "description": "Media evaluation task. POMP score for the fake news items (awareness)",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_awareness_pomp",
      "description": "Media evaluation task. POMP score for the real news items (awareness)",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_accuracy_pomp",
      "description": "Media evaluation task. POMP score for the fake news items (accuracy)",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_accuracy_pomp",
      "description": "Media evaluation task. POMP score for the real news items (accuracy)",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_sharing_pomp",
      "description": "Media evaluation task. POMP score for the fake news items (sharing)",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_sharing_pomp",
      "description": "Media evaluation task. POMP score for the real news items (sharing)",
      "@type": "propertyValue"
    },
    {
      "name": "IAT_D2_recoded_for_source_valence",
      "description": "IAT_D2 recoded for source_valence. If source_valence == \"negative\", IAT_D2*-1, otherwise IAT_D2.",
      "@type": "propertyValue"
    },
    {
      "name": "mean_self_reported_evaluation_recoded_for_source_valence",
      "description": "mean_self_reported_evaluation recoded for source_valence. If source_valence == \"negative\", mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.",
      "@type": "propertyValue"
    },
    {
      "name": "mean_intentions_recoded_for_source_valence",
      "description": "mean_intentions recoded for source_valence. If source_valence == \"negative\", mean_intentions*-1, otherwise mean_intentions.",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_concept_check",
      "description": "Question assessin if participants were aware of the concept of Deepfaking before taking part in the study",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_detected_rater_1",
      "description": "Ratings indicating whether a participant detected that they were exposed to Deepfaked content during the study. Ratings obtained from Rater 1",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_detected_rater_2",
      "description": "Ratings indicating whether a participant detected that they were exposed to Deepfaked content during the study. Ratings obtained from Rater 2",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_concept_check_rater_1",
      "description": "Ratings indicating if participants were aware of the concept of a Deepfake before  taking part in the study. Ratings obtained from Rater 1",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_concept_check_rater_2",
      "description": "Ratings indicating if participants were aware of the concept of a Deepfake before  taking part in the study. Ratings obtained from Rater 2",
      "@type": "propertyValue"
    },
    {
      "name": "gender_self_describe",
      "description": "Open ended gender",
      "@type": "propertyValue"
    },
    {
      "name": "ethnicity",
      "description": "Ethnicity",
      "@type": "propertyValue"
    },
    {
      "name": "ethnicity_self_describe",
      "description": "Open ended ethnicity",
      "@type": "propertyValue"
    },
    {
      "name": "location",
      "description": "Location",
      "@type": "propertyValue"
    },
    {
      "name": "education",
      "description": "Education",
      "@type": "propertyValue"
    },
    {
      "name": "employment",
      "description": "Employment status",
      "@type": "propertyValue"
    },
    {
      "name": "education_recoded",
      "description": "Education recoded into seven groups",
      "@type": "propertyValue"
    },
    {
      "name": "income",
      "description": "Income level",
      "@type": "propertyValue"
    },
    {
      "name": "income_recoded",
      "description": "Income level recoded into 7 groups",
      "@type": "propertyValue"
    },
    {
      "name": "political_ideology_identity",
      "description": "4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about the importance of political ideology to one's self-identity",
      "@type": "propertyValue"
    },
    {
      "name": "political_ideology_social_issues",
      "description": "4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about how liberal vs. conservative they are when it comes to social issues.",
      "@type": "propertyValue"
    },
    {
      "name": "political_ideology_economic_issues",
      "description": "4 item-measure of political ideology developed by Pennycook and Rand (2018). This measure combines the two questions about how liberal vs. conservative they are when it comes to economic issues.",
      "@type": "propertyValue"
    },
    {
      "name": "religious_affiliation_general",
      "description": "Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). Raw responses",
      "@type": "propertyValue"
    },
    {
      "name": "religious_affiliation_general_recoded",
      "description": "Religious Affiliation Scale (Pennycook Cheyne Barr Koehler & Fugelsang 2014). Participants categorised into one of three groups (Agnostic Atheist Religious)",
      "@type": "propertyValue"
    },
    {
      "name": "passed_iat_performance",
      "description": "Mark participants for exclusion if their total error rate is >30% their error rate in any one block is >40% or if >10% RTs are <300ms.",
      "@type": "propertyValue"
    },
    {
      "name": "complete_iat",
      "description": "Complete IAT data (used for exclusions)",
      "@type": "propertyValue"
    },
    {
      "name": "complete_selfreport",
      "description": "Complete self reported evaluations data (used for exclusions)",
      "@type": "propertyValue"
    },
    {
      "name": "complete_intentions",
      "description": "Complete behavioural intentions data (used for exclusions)",
      "@type": "propertyValue"
    },
    {
      "name": "task_order",
      "description": "Did the particiapant complete the self-reported evaluations or the IAT first?",
      "@type": "propertyValue"
    },
    {
      "name": "iat_block_order",
      "description": "Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g. if source_valence was negative did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?",
      "@type": "propertyValue"
    }
  ]
}`

Write alternative file types to disk

Original csv file is used for analyses (as it is simplest), but other file types that integrate the labels are likely to be more useful for reuse.

I include an R .rds file (which includes data labels and data types), SPSS .sav and Stata .dta files.

write_rds(codebook_data, "processed/4_data_participant_level_with_hand_scoring.rds") 
rio::export(codebook_data, "processed/4_data_participant_level_with_hand_scoring.sav") # SPSS file
rio::export(codebook_data, "processed/4_data_participant_level_with_hand_scoring.dta") # Stata file

Session Info

sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rio_0.5.16       labelled_2.7.0   codebook_0.9.2   kableExtra_1.3.1
##  [5] knitr_1.30       forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2     
##  [9] purrr_0.3.4      readr_1.3.1      tidyr_1.1.2      tibble_3.0.3    
## [13] ggplot2_3.3.2    tidyverse_1.3.0 
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.1        jsonlite_1.7.1    viridisLite_0.3.0 modelr_0.1.8     
##  [5] assertthat_0.2.1  highr_0.8         cellranger_1.1.0  yaml_2.2.1       
##  [9] globals_0.13.1    pillar_1.4.6      backports_1.1.9   glue_1.4.2       
## [13] digest_0.6.25     rvest_0.3.5       colorspace_1.4-1  htmltools_0.5.0  
## [17] pkgconfig_2.0.3   broom_0.7.2       listenv_0.8.0     haven_2.3.1      
## [21] scales_1.1.1      webshot_0.5.2     openxlsx_4.1.5    generics_0.0.2   
## [25] farver_2.0.3      ellipsis_0.3.1    DT_0.13           withr_2.2.0      
## [29] repr_1.1.0        skimr_2.1.2       cli_2.0.2         rmdpartials_0.5.8
## [33] magrittr_1.5      crayon_1.3.4      readxl_1.3.1      evaluate_0.14    
## [37] fs_1.4.1          future_1.19.1     fansi_0.4.1       xml2_1.3.2       
## [41] foreign_0.8-80    tools_4.0.2       data.table_1.13.2 hms_0.5.3        
## [45] lifecycle_0.2.0   munsell_0.5.0     reprex_0.3.0      zip_2.1.1        
## [49] compiler_4.0.2    rlang_0.4.8       grid_4.0.2        rstudioapi_0.11  
## [53] htmlwidgets_1.5.1 crosstalk_1.1.0.1 base64enc_0.1-3   labeling_0.3     
## [57] rmarkdown_2.5     gtable_0.3.0      codetools_0.2-16  DBI_1.1.0        
## [61] curl_4.3          R6_2.4.1          lubridate_1.7.9   stringi_1.4.6    
## [65] parallel_4.0.2    Rcpp_1.0.5        vctrs_0.3.4       dbplyr_1.4.3     
## [69] tidyselect_1.1.0  xfun_0.19